Fh_Okcsr_Roos_2022_study2_Feeling_Heard

40,854 responses from 1,000 respondents to 102 items.

One of 2 tables from the same source; this one is the study2_Feeling_Heard table. The others are listed under Related tables.

About this table

DescriptionFeeling heard scale
ReferenceRoos, C. A., Postmes, T., & Koudenburg, N. (2023). Feeling heard: Operationalizing a key concept for social relations. Plos one, 18(11), e0292865. Roos, Carla; Postmes, Tom; Koudenburg, Namkje, 2022, "Feeling heard: Operationalizing a key concept for social relations", https://doi.org/10.34894/IHNKUN, DataverseNL, V2
LicenceCC0 1.0
Source datahttps://dataverse.nl/dataset.xhtml?persistentId=doi:10.34894/IHNKUN

Size and shape

Responses40,854
Respondents1,000
Items102
Response categories9
Responses per respondent40.854
Responses per item400.529
Density0.401
LongitudinalFALSE

Classification

age rangeAdult (18+)
sampleInternet-based, Representative
construct typeAffective/mental health
measurement toolSurvey/questionnaire
item formatLikert Scale/selected response
primary language(s)eng
construct nameFeeling heard scale

Codebook

This codebook is reconstructed by the IRW from the IRW data standard and the script that built it, Fh_Okcsr_Roos_2022_study2_Feeling_CSV.R. It is our best reading, not the source’s own codebook, and it may contain mistakes. Where the two disagree, the source data are the authority. If you find an error, please tell us.

ColumnWhat it isSource column
IRW standard columns
idIRW standard Identifier for the focal unit being measured — typically a person, but sometimes another entity (e.g., a word in a lexical task).made in the build script
itemIRW standard Item identifier.made in the build script
respIRW standard Response value.made in the build script
Other columns
groupNot documented Not documented by the IRW; see the source’s codebook.not traced

What item codes and covariate values stand for is in the source’s codebook, Codebook Feeling heard Studies 1, 2 and 3.pdf.

Get the data

Download CSVno account neededBrowse on Redivisexplore and queryCroissant metadataHugging Face, Kaggle, OpenML

Or load it directly in R or Python (both need a free Redivis account; the first call opens a browser to sign in):

# R
install.packages("remotes")
remotes::install_github("itemresponsewarehouse/Rpkg")
library(irw)
df <- irw_fetch("Fh_Okcsr_Roos_2022_study2_Feeling_Heard")
# Python
pip install irw

import irw
df = irw.fetch("Fh_Okcsr_Roos_2022_study2_Feeling_Heard")

How to cite

@misc{https://doi.org/10.34894/ihnkun,
  doi = {10.34894/IHNKUN},
  url = {https://dataverse.nl/citation?persistentId=doi:10.34894/IHNKUN},
  author = {Roos, Carla and Postmes, Tom and Koudenburg, Namkje},
  title = {Feeling heard: Operationalizing a key concept for social relations},
  publisher = {DataverseNL},
  year = {2022}
}

Version and provenance

IRW versionv557
Redivis datasetitem_response_warehouse v70.0
Redivis dataset DOI10.57761/4qxs-pq87
Manifest pin for this IRW versionv70.0
Metadata sourceirw_meta v40.0